The present disclosure generally relates to environmental control systems, such as heating, ventilation, and air conditioning (HVAC) systems, which can be used to control the temperature and humidity of common spaces, e.g., as can exist in data centers containing server computers. More, specifically the present disclosure relates to an optimization method for determining a change to the operation levels of actuators of the environmental control system.
Modern datacenters use HVAC or computer room air conditioner (CRAC) systems to control indoor temperature, humidity, and other variables. It is common to have many HVAC units deployed throughout a data center. They are often floor-standing units, but may be wall-mounted, rack-mounted, or ceiling-mounted. The HVAC units also often provide cooled air either to a raised-floor plenum, to a network of air ducts, or to the open air of the data center. The data center itself, or a large section of a large data center, typically has an open-plan construction, i.e. no permanent partitions separating the air in one part of the data center from the air in another part. Thus, in many cases, these data centers have a common space that is temperature-controlled and humidity-controlled by multiple HVAC units.
HVAC units for data centers are typically operated with decentralized, stand-alone controls. It is common for each unit to operate in an attempt to control the temperature and humidity of the air entering the unit from the data center. For example, an HVAC unit may contain a sensor that determines the temperature and humidity of the air entering the unit. Based on the measurements of this sensor, the controls of that HVAC will alter operation of the unit in an attempt to change the temperature and humidity of the air entering the unit to align with the set points for that unit.
For reliability, most data centers are designed with an excess number of HVAC units. Since the open-plan construction allows free flow of air throughout the data center, the operation of one unit can be coupled to the operation of another unit. The excess units and the fact that they deliver air to substantially overlapping areas provides a redundancy, which ensures that if a single unit fails, the data center equipment (servers, routers, etc.) will still have adequate cooling to maintain the temperature within a desired set point.
As mentioned above, the standard operating procedure for the HVAC units is to control the return air temperature into each HVAC unit. Such operation is not aware of the temperature of the air entering the servers and other computing equipment in the data center. Since this information is not available to the HVAC unit controls or to the data center operators, the extra HVAC units are typically run at all times, to ensure that the servers are kept cool. However, such precautionary measures waste energy.
While it is possible to manually turn off redundant HVAC units to save energy, doing so poses a risk of overheating that equipment. Also, some data centers have variable loads, in which case it may be necessary to stop and start several units throughout the day. Furthermore, the complex and tightly coupled airflow patterns in a data center make it difficult for an operator to know which HVAC unit will be the most effective to restart should a need be identified.
Additionally, current methods ignore the cost (e.g., energy usage or maintenance) of HVAC units when determining how to control the units. U.S. application Ser. No. 13/215,189 describes methods for incorporating costs of operating an environmental maintenance system to determine optimal operation levels of the actuators of the system. However, the determination of optimal operation levels can be difficult when there are many actuators and/or when there are many operation levels (e.g. many settings) for the actuators. This difficulty is compounded when the sensor values are constrained to be within a desired range.
Nelder-Mead (aka Amoeba) type generic search algorithms are well known and widely used. They comprise a multidimensional geometric simplex and a set of rules for moving and reshaping that simplex through the optimization space. These methods are not well suited for problems with a very large number of constraints and very large-dimensional optimization spaces due to the fact that the algorithm relies on a measure of distance between two points in the optimization space (e.g., a sum of squares of the Cartesian coordinate differences between the two points). When there are very many coordinates (e.g., 100), then the individual contribution of one coordinate difference becomes very small. As a result, small variations in the shape of the Nelder-Mead simplex are difficult to distinguish from each other and the simplexes can quickly degenerate into a nearly zero-volume simplex.
Another optimization method is a pattern search algorithm that uses a multidimensional cross that grows and shrinks as it is moved through the optimization space. The cost at each end of the cross is compared with the cost at the center of the cross to decide where to move the cross next and how to grow/or shrink it. The basic shape does not change unlike the Nelder-Mead simplex so that this approach works much better in large-dimensional optimization problems. However, the pattern search is not well suited to problems with a very large number of constraints, especially if these constrains can potentially carve out extremely complicated, and possibly disjoint, subspaces of allowable control value combinations.
Therefore, it is desirable to provide new methods and systems that can determine optimal operation levels for actuators of an environmental maintenance system, e.g., a method that can produce near-optimal solutions to optimization problems that are characterized by a very large number of controls and constraints on controls and sensor values, preferably in a predictable timeframe.